--- title: Race to the rock author: nige date: '2018-09-19' cover: "mawsontrail.jpg" bibliography: ["packageCitations.bib"] categories: - Bike - R tags: - dotwatcher - mapprogress - racetotherock - data ---

“It’s safer, less painful and less soul destroying to remain a dot watcher.”

With those words for inspiration, this post takes dot watching several levels too far.

For an introduction to Race to the Rock perhaps read about the first 2016 ride. Race to the rock happened again in 2017 and 2018. There’s an active online community serving up a constant stream of visual inspiration. And there’s this from northsouth. Wow. Anyway, this post is a little different, but hopefully adds another tasty tidbit to the race-to-the-rock soup.

Each rider carries a live gps tracker that, in combination with map progress website, provides easily accessible data. Roughly each 10 minutes you can find out where the rider was, the distance covered since the last data point and a few other bits of information. (The full data for 2018 is available here). Raw data shows what this raw data looks like. Some quick summary stats:

What other insights can the dot watcher gain from playing with these data…

Distance

Figure 1 shows the cumulative distance by date. The purple dot is consistent!

Cumulative distance travelled by the four finishers

Figure 1: Cumulative distance travelled by the four finishers

Speeds

Figure 2 shows the distribution of speed in km/h. This is the average speed over each (roughly) 10 minute interval between data points. The singlespeeder certainly had a more pointy distribution than the other riders. And perhaps spent a lot of time walking (about 5 km/h) in the first week?

Erinn seemed to find a bit more top-end speed than the others, with 27 intervals above 30 km/h vs 7 for Sarah, 14 for Nick and 2 for Emma.

Distribution of speed

Figure 2: Distribution of speed

Stoping times

Sarah spent much more time moving than the other finishers!

Total stopping time

Figure 3: Total stopping time

Map

Mapping the speed by each data point results in the following maps. The map is set to zoom on about Leigh Creek, but you can pan/zoom anywhere.

Wrap-up

And at the end of all those dots - the rock for the few who have finished!

View this post on Instagram

Done and dusted. 3rd overall and only SS finisher in the 3 years (citation needed). Despite being unsupported, the generous actions of many made sure I arrived in a timely matter. The list of people to thank is nearly as many as the extra incidental kilometers I rode. Firstly thank you to (???) being the mastermind behind the concept, (???) for not only displaying an incredible strength and resilience, but also her help with preparation of the event. Fellow competitors (???) (???) (???) (???) and Erinn for displaying the grit and bravery required to undertake the task. All the friends and family (and everyone else) sent messages, took photos and came for a ride. All the travellers and shop/roadhouse staff for breaking their own rules with leaving food out, letting me sleep on their deck/couch. As well as the First Nations, and community groups who look after the Tasmanian/Goldfields/Mawson trails.

A post shared by Nick ‘the scary jew’ Skarajew ((???)) on

Raw data

The following table shows the first few records for each rider. If the table options are hard to see, press the lightbulb at the top-right of the page to switch to ‘light’ mode.

Acknowledgements

Table 1: R packages explicitly loaded (i.e. not including dependencies) to create this post
Package Description Citation
bookdown bookdown: Authoring Books and Technical Documents with R Markdown Xie (2018)
DT DT: A Wrapper of the JavaScript Library ‘DataTables’ Xie, Cheng, and Tan (2018)
ggridges ggridges: Ridgeline Plots in ‘ggplot2’ Wilke (2018)
knitr knitr: A General-Purpose Package for Dynamic Report Generation in R Xie (2019)
lubridate lubridate: Make Dealing with Dates a Little Easier Spinu, Grolemund, and Wickham (2018)
SearchTrees SearchTrees: Spatial Search Trees Becker (2012)
sf sf: Simple Features for R Pebesma (2019)
tidyverse tidyverse: Easily Install and Load the ‘Tidyverse’ Wickham (2017)
tmap tmap: Thematic Maps Tennekes (2019)
tmaptools tmaptools: Thematic Map Tools Tennekes (2018)
widgetframe widgetframe: ‘Htmlwidgets’ in Responsive ‘iframes’ Karambelkar (2017)

Citations

Becker, Gabriel. 2012. SearchTrees: Spatial Search Trees. https://CRAN.R-project.org/package=SearchTrees.

Karambelkar, Bhaskar. 2017. Widgetframe: ’Htmlwidgets’ in Responsive ’Iframes’. https://CRAN.R-project.org/package=widgetframe.

Pebesma, Edzer. 2019. Sf: Simple Features for R. https://CRAN.R-project.org/package=sf.

Spinu, Vitalie, Garrett Grolemund, and Hadley Wickham. 2018. Lubridate: Make Dealing with Dates a Little Easier. https://CRAN.R-project.org/package=lubridate.

Tennekes, Martijn. 2018. Tmaptools: Thematic Map Tools. https://CRAN.R-project.org/package=tmaptools.

———. 2019. Tmap: Thematic Maps. https://CRAN.R-project.org/package=tmap.

Wickham, Hadley. 2017. Tidyverse: Easily Install and Load the ’Tidyverse’. https://CRAN.R-project.org/package=tidyverse.

Wilke, Claus O. 2018. Ggridges: Ridgeline Plots in ’Ggplot2’. https://CRAN.R-project.org/package=ggridges.

Xie, Yihui. 2018. Bookdown: Authoring Books and Technical Documents with R Markdown. https://CRAN.R-project.org/package=bookdown.

———. 2019. Knitr: A General-Purpose Package for Dynamic Report Generation in R. https://CRAN.R-project.org/package=knitr.

Xie, Yihui, Joe Cheng, and Xianying Tan. 2018. DT: A Wrapper of the Javascript Library ’Datatables’. https://CRAN.R-project.org/package=DT.